Openmetadata Datahub And Openlineage

Community

vaquarkhan

openmetadata-datahub-and-openlineage

Guides agents through metadata platform and lineage workflows using OpenMetadata, DataHub, or OpenLineage-compatible systems. Use when improving discovery, lineage quality, metadata governance, or producer-to-catalog integration.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`openmetadata-datahub-and-openlineage`
Stars	60
Forks	8
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by vaquarkhan on GitHub. Read the source before you install it.

Installation

Install the Openmetadata Datahub And Openlineage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/openmetadata-datahub-and-openlineage
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/openmetadata-datahub-and-openlineage

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skills.git /tmp/data-engineering-agent-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-agent-skills/skills/openmetadata-datahub-and-openlineage .claude/skills/openmetadata-datahub-and-openlineage

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openmetadata Datahub And Openlineage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Openmetadata Datahub And Openlineage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Openmetadata Datahub And Openlineage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenMetadata DataHub And OpenLineage

Overview

Use this skill when metadata and lineage must be operationalized through open tooling such as OpenMetadata, DataHub, or OpenLineage. It helps agents align producers with catalog expectations, ensure lineage accuracy, and make discovery trustworthy rather than decorative.

When to Use

integrating metadata platforms into delivery workflows
improving dataset discovery and trust signals
capturing and validating lineage across pipelines
publishing governed datasets into open catalog ecosystems
connecting OpenLineage events from Spark, Airflow, or dbt to a catalog
defining metadata governance policies for multi-team environments

Do not use this when the team has no shared catalog requirement or when lineage is handled entirely within a closed platform (e.g., Unity Catalog, Purview) with no open integration needs.

Workflow

Define metadata ownership and minimum required fields. Include:
- who owns each dataset's metadata (producing team, platform team, or steward)
- minimum required fields: owner, description, classification, freshness, grain
- which fields are auto-populated from lineage vs manually maintained
- how metadata quality is measured and enforced
- what "complete" means for a dataset to appear as discoverable
Connect lineage capture to actual execution surfaces.
- OpenLineage integration with orchestrators (Airflow, Dagster)
- OpenLineage integration with processing engines (Spark, dbt, Flink)
- validate that lineage events fire correctly on job start, complete, and failure
- test that column-level lineage propagates through transformation layers
- monitor for gaps: jobs that run but produce no lineage events
Make discovery trustworthy through quality signals.
- surface trust indicators: last validated, freshness, quality score, owner responsiveness
- distinguish production-governed datasets from experimental or deprecated ones
- tag datasets with classification levels (public, internal, restricted, sensitive)
- ensure search relevance: descriptions, tags, and usage signals improve discoverability
- retire stale assets — dead datasets in the catalog erode trust
Integrate metadata publishing into the pipeline lifecycle.
- metadata should update when pipelines deploy, not in separate manual workflows
- use CI/CD hooks to validate metadata completeness before release
- publish schema changes to the catalog automatically through lineage events
- trigger alerts when metadata freshness falls behind actual data freshness
- make metadata a release-gate requirement: no catalog entry, no production access
Plan for multi-team governance and federated ownership.
- define roles: data producer, data steward, platform admin, consumer
- establish policies for who can modify metadata for shared datasets
- create templates for common dataset types so teams start with good defaults
- define escalation paths when metadata quality issues are found
- audit metadata coverage regularly and report gaps per team
Handle lineage accuracy and drift.
- lineage can become stale when pipelines change without updating integration
- schedule lineage validation: compare catalog lineage with actual pipeline structure
- alert when expected lineage events stop firing
- plan for lineage in complex scenarios: dynamic tables, views, materialized CTEs
- document lineage limitations: what the system captures vs what it cannot

Common Rationalizations

Rationalization	Reality
"We'll add metadata later after the pipeline is stable."	Metadata added later is often wrong because context is lost. Capturing metadata during development is cheaper and more accurate.
"Lineage is automatic — we just need to install the integration."	Integrations capture lineage from supported execution paths only. Custom code, dynamic SQL, and non-instrumented tools create gaps that require explicit attention.
"The catalog is just for discovery — it doesn't need to be accurate."	An inaccurate catalog is worse than no catalog. Teams lose trust and stop using it, making the investment worthless.
"Metadata governance is overhead for small teams."	Small teams benefit most from metadata discipline because there's less institutional knowledge to fall back on when someone leaves.

Red Flags

catalog has datasets with no owner, no description, or stale freshness indicators
lineage graph has gaps for major pipelines or transformation layers
metadata is manually maintained in a spreadsheet separate from the platform
no process for retiring deprecated or stale datasets from the catalog
lineage integration is installed but nobody validates its output
teams publish data without any catalog entry or metadata requirement
column-level lineage is claimed but never tested for accuracy
metadata quality is never measured or reported

Verification

Metadata ownership is defined and minimum required fields are enforced
Lineage events fire correctly from orchestrators and processing engines
Column-level lineage is validated for critical transformation paths
Discovery trust signals (freshness, quality, classification) are surfaced
Metadata publishing is integrated into the pipeline CI/CD lifecycle
Stale and deprecated assets are regularly retired from the catalog
Lineage accuracy is audited and gaps are tracked per pipeline

Frequently asked questions

What does the Openmetadata Datahub And Openlineage AI skill do?

Why use Openmetadata Datahub And Openlineage on TypingMind?

Because you install it once and use it with any model. Openmetadata Datahub And Openlineage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Openmetadata Datahub And Openlineage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/openmetadata-datahub-and-openlineage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Openmetadata Datahub And Openlineage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Openmetadata Datahub And Openlineage?

As many as you like. As long as a model supports skills, you can use Openmetadata Datahub And Openlineage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Openmetadata Datahub And Openlineage AI skill free?

Yes. It is published on GitHub by vaquarkhan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

OpenMetadata DataHub And OpenLineage

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

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